Segmentation of intersection components based on aerial photos and point cloud data

Abstract

Intersection geometry diagram including road markings are important data for advancing the digital transformation (DX) of road administration. There is a need to establish efficient data collection and generation methods. Previously, a method of generating diagrams using semantic segmentation (SS) based on aerial photographs has been proposed by the authors, but the challenge has been remained that the accuracy in areas with occlusions such as pedestrian bridges declines. To overcome the defect of the previous methodology, this study proposes a method that combines 3D point cloud data and aerial photographs to perform semantic segmentation considering occlusions. This method identifies occlusion objects from the 3D point cloud data, projects their latitude and longitude information onto the aerial photographs, masks the corresponding pixels, and performs SS inference. As a result of the verification, it was clarified that it is possible to identify the components of intersections around occlusions accurately, and that there is an appropriate point cloud density that maximizes identification accuracy.